• International conference on construction engineering and project management
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• 2022.06a
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• pp.1256-1263
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• 2022

With the advent of the Fourth Industrial Revolution, the amount of energy used in buildings has been increasing due to changes in the energy use structure caused by the massive spread of information-oriented equipment, climate change and greenhouse gas emissions. For the efficient use of energy, it is necessary to have a plan that can predict and reduce the amount of energy use according to the type of energy source and the use of buildings. To address such issues, this study presents a model embedded in a digital twin that predicts energy use in buildings. The digital twin is a system that can support a solution of urban problems through the process of simulations and analyses based on the data collected via sensors in real-time. To develop the energy use prediction model, energy-related data such as actual room use, power use and gas use were collected. Factors that significantly affect energy use were identified through a correlation analysis and multiple regression analysis based on the collected data. The proof-of-concept prototype was developed with an exhibition facility for performance evaluation and validation. The test results confirm that the error rate of the energy consumption prediction model decreases, and the prediction performance improves as the data is accumulated by comparing the error rates of the model. The energy use prediction model thus predicts future energy use and supports formulating a systematic energy management plan in consideration of characteristics of building spaces such as the purpose and the occupancy time of each room. It is suggested to collect and analyze data from other facilities in the future to develop a general-purpose energy use prediction model.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.21-33
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• 2024

Recently, the patterns of climate change-induced disasters have become more diverse and extensive. To develop an effective flood control plan, Korea has incorporated the concept of Potential Flood Damage (PFD) into the Long-Term Comprehensive Water Resources Plan to assess flood risk. However, concerns regarding the PFD have prompted numerous studies. Previous research primarily focused on modifying and augmenting the PFD index or introducing new indices. This study aims to enhance the existing flood control safety evaluation method by utilizing a flood risk map that incorporates risk indices, specifically focusing on the Yeong-Seomjin river basin. The study introduces three main evaluation approaches: risk and potential analysis, PFD and flood management level analysis, and flood control safety evaluation. The proposed improved evaluation method is expected to be instrumental in evaluating various flood control safety measures and formulating flood control plans.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.19-24
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• 2024

Global climate change is causing an increase in natural disasters around the world due to long-term temperature increases and changes in rainfall. Among them, forest fires are becoming increasingly large. South Korea experienced an average of 537 forest fires over a 10-year period (2013-2022), burning 3,560 hectares of forest. That's 1,180 soccer fields(approximately 3 hectares) of forest burning every year. This paper proposed an artificial intelligence based wildfire detection method using image and text information. The performance of the proposed method was compared with YOLOv9-C, RT-DETR-Res50, RT-DETR-L, and YOLO-World-S methods for mAP50, mAP75, and FPS, and it was confirmed that the proposed method has higher performance than other methods. The proposed method was demonstrated as a forest fire detection model of the early forest fire detection system in the Gangwon State, and it is planned to be advanced in the direction of fire detection that can include not only forest areas but also urban areas in the future.

• Geomechanics and Engineering
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• v.39 no.2
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• pp.181-195
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• 2024

Existing piles, especially in urban areas, are at risk of being cut by new tunnel construction, potentially affecting their serviceability. This study examined the behaviour of piles under various reinforcement conditions subject to tip cutting resulting from tunnel excavation. For this, the construction of a tunnel using a shield tunnel boring machine adjacent to existing single and group piles was simulated. A three-dimensional finite element analysis was used to perform the simulations. Certain piles in the group were simulated by cutting the pile tips to mimic the effect of tunnel excavation, and the behaviour of the piles was studied by considering the effect of pile cap and ground reinforcements. A numerical analysis was used to examine the ground settlement caused by tunnel excavation, pile head settlement, axial pile force, and shear stress occurring at the pile-ground interface. The results revealed that for all piles with pile tips supported by weathered rock, the shear stress distributions demonstrated similar trends, whereas for piles with cut tips, tensile or compressive forces occurred simultaneously according to the relative position by pile depth. Additionally, when the pile tip was supported by weathered rock, approximately 70% of the support was due to shaft friction and the remaining 30% was provided by the pile tip. For piles without reinforcement, the final settlement was approximately 70% greater than that of piles with grouting reinforcement. These results indicate that pile and ground settlements are substantially influenced by pile tip cutting and reinforcement conditions.

• Ecology and Resilient Infrastructure
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• v.7 no.2
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• pp.126-133
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• 2020

In accordance with the implementation of the sunset provision to cancel the designations of urban park sites that remained unexecuted for a prolonged period until 2020, the park sites in the city center, which account for 90% of the long-term unexecuted urban facilities subjected to the provision, are currently on the verge of development. The total area of the 204 park sites that will disappear in Seoul as a result of this provision is 95 ㎢; moreover, 116 of these are privately-owned. It is expected that the possible changes in the use of these park sites could result in reckless development and reduction of green space, which would ultimately affect the ecosystem. This study applied the InVEST model to calculate the changes in the fixed carbon amount before and after the implementation of the sunset provision to estimate the economic value of these changes. The study focused on Jongno-gu in Seoul because it has the most unexecuted park sites subjected to the lifting of the designation. The research findings show that the fixed carbon amount provided by the unexecuted park sites in Jongno-gu was 374,448 mg, prior to the implementation of the sunset provision; however, the amount was estimated to decrease by 18% to 305,564 mg after its execution. When calculated in terms of average value of the real carbon price, this translated into a loss of approximately 700 million won. In addition, considering the social costs including both climate change and the impact on the ecosystem, an economic loss of approximately 98 billion won was projected. This study is meaningful because its predictions are based on the estimation of fixed carbon amount according to the implementation of the sunset provision in Jongno-gu and scientifically calculates the value of ecological services provided by the parks in the city. This study can serve not only as a basis during the decision-making process for policies related to ecosystem conservation and development, but also as an evidentiary material for the compensation of privately-owned land that is designated as urban park sites and was unexecuted for a prolonged period.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.12
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• pp.874-885
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• 2011

In this study, the urban $CO_2$ emission based on energy consumption (Coal, Petroleum, Electricity, and City Gas) in 16 provincial and metropolitan city governments in South Korea was evaluated. For calculation of the urban $CO_2$ emission, direct and indirect emissions were considered. Direct emissions refer to generation of greenhouse gas (GHG) on-site from the energy consumption. Indirect emissions refer to the use of resources or goods that discharge GHG emissions during energy production. The total GHG emission was 497,083 thousand ton $CO_2eq.$ in 2007. In the indirect GHG emission, about 240,388 thousand ton $CO_2eq.$ was occurred, as 48% of total GHG emission. About 256,694 thousand ton $CO_2eq.$ (52% of total GHG emissions) was produced in the direct GHG emission. This amount shows 13% difference with 439,698 thousand ton $CO_2eq.$ which is total national GHG emission data using current calculation method. Local metropolitan governments have to try to get accuracy and reliability for quantifying their GHG emission. Therefore, it is necessary to develop and use Korean emission factors than using the IPCC (Intergovernmental Panel on Climate Change) emission factors. The method considering indirect and direct GHG emission, which is suggested in this study, should be considered and compared with previous studies.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.79-105
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• 2023

Due to unprecedented extreme weather events such as global warming and climate change, many parts of the world suffer from severe pain, and economic losses are also snowballing. In order to address these problems, 'The Paris Agreement' was signed in 2016, and an intergovernmental consultative body was formed to keep the average temperature rise of the Earth below 1.5℃. Korea also declared 'Carbon Neutrality in 2050' to prevent climate catastrophe. In particular, it was found that the increase in temperature caused by greenhouse gas emissions hurts the environment and society as a whole, as well as the export-dependent economy of Korea. In addition, as the diversification of transportation types is accelerating, the change in means of choice is also increasing. As the development paradigm in the low-growth era changes to urban regeneration, interest in idle railway sites is rising due to reduced demand for routes, improvement of alignment, and relocation of urban railways. Meanwhile, it is possible to partially achieve the solar power generation goal of 'Renewable Energy 3020' by utilizing already developed but idle railway sites and take advantage of being free from environmental damage and resident acceptance issues surrounding the location; but the actual use and plan for these solar power facilities are still lacking. Therefore, in this study, using the big data provided by the Korea National Railway and the Renewable Energy Cloud Platform, we develop an algorithm to discover and analyze suitable idle sites where solar power generation facilities can be installed and identify potentially applicable areas considering conditions desired by users. By searching and deriving these idle but relevant sites, it is intended to devise a plan to save enormous costs for facilities or expansion in the early stages of development. This study uses various cluster analyses to develop an optimal algorithm that can derive solar power plant locations on idle railway sites and, as a result, suggests 202 'actively recommended areas.' These results would help decision-makers make rational decisions from the viewpoint of simultaneously considering the economy and the environment.

• Fire Science and Engineering
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• v.27 no.3
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• pp.1-7
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• 2013

The wildland fire intensity and scale are getting bigger owing to climate change in the world. In the case of domestic, the forest is distributed over approximately 63.7 % of country and the main facilities like a industrial facility or gas facility abuts onto it. Therefore there is potential that the wildland fire is developed to a large-scale disaster. In this study, the effect distances of the radiant heat flux from the crown fire are analysed according to the change of wind velocity. The safety criteria concerning the radiant heat flux to influence on the surrounding were researched to analyse the effect distances. The criteria of radiant heat flux were chosen $5kW/m^2$, $12.5kW/m^2$, $37.5kW/m^2$. WFDS, which is an extension of NIST's Fire Dynamics Simulator, was used to consequence analysis of the forest fire. In order to apply the analysis conditions, it is researched the forest conditions that is generally distributed in domestic region. As the result, the maximum effect distances by radiant heat were showed at the horizontal and vertical direction. When the wind velocity varied from 0 to 10 m/s, the maximum effect distance increased as the wind velocity increases. Interesting point is that the maximum effect distance were shown at the wind velocity of 8 m/s. The maximum effect distance was decreased according as the fuel moisture of trees increase. This study can contribute to analyse quantitative risk about the damage effect of the surrounding facilities caused by wildland fire.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.93-93
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• 2011

전 세계적으로 기후변화로 인한 기상재해의 피해가 매년 증가하고 있으며, 기후변화로 인한 시민들의 안전, 재산, 인명피해 또한 늘어나고 있다. 이러한 피해를 최소화하기 위해서는 도시지역을 중심으로 한 신뢰성 높은 미래 기후전망 기법이 필수적이며, 미래 기후전망을 바탕으로 하여 기후변화로 인한 향후 발생할 수 있는 위험성의 정도를 전망하여 적응대책을 수립할 필요가 있다. 본 연구에서는 도시지역의 미래 기후전망 기법을 개발하여 서울시의 미래기후를 전망한다. 본 연구를 수행하기 위하여 먼저 IPCC 기후시나리오에 대한 조사를 수행하여 자료를 수집한다. 수집한 자료를 바탕으로 역학적 상세화와 통계적 상세화 기법을 이용하여 고해상도 기후 시나리오를 생산하였다. 역학적 상세화 기법은 A2시나리오의 ECHO-G/S에서 생산된 기후 시나리오를 이용하여 지역 기후모델인 RegCM3에 적용하여 상세화 과정을 수행하였다. RegCM3를 이용하여 60km로 상세화한 후에 one-way double-nested system을 구축하여 20km까지 상세화 하였다. 20km 해상도의 기후 시나리오는 서울시와 같은 좁은 지역의 기후를 분석하기에는 어려움이 있으므로, RegCM3에 사용할 수 있는 Sub-BATS라는 기법을 이용하여 5km의 고해상도 기후 시나리오를 생산하였다. 역학적 상세화 결과는 관측결과에 비해 과소 추정되는 경향이 있어, 편차보정을 통하여 관측값에 가까운 자료를 만들어 주었다. 역학적 상세화 결과를 분석한 결과, 기준기간에 비해 미래기간(S3)에는 전체적으로 약 4.9도의 기온상승과 강수량 증가가 나타났으며, 특히 9월에 가장 큰 상승폭을 나타내고 있었다. 강수량의 경우 증가 경향이 뚜렷이 나타나고 있었으며, 여름철에 큰 증가폭을 나타내고 있었다. 통계적 상세화 기법은 역학적 상세화 기법에서 사용된 ECHO-G/S를 포함한 13개의 GCM결과와 우리나라의 57개 지점에 대한 CSEOF기법을 이용하여 기후 시나리오를 생산하였다. 이 자료는 서울시에 대하여 하나의 지점밖에 존재하지 않아, 서울시내의 지역별 미래 기후전망에는 문제가 있었으므로, Delta method라는 기법을 이용하여 서울 및 인근지역의 AWS 35개 지점에 대하여 미래 기후시나리오를 생산하였다. 통계적 상세화 결과, 13개 GCM의 기온변화는 전체평균 약 3.1도 상승하였고, 겨울과 여름철의 변화폭이 가장 크며, 모델의 불확실성 또한 겨울과 여름에 가장 큰 특징을 가지고 있다. 강수량의 경우 MME에서는 약간의 상승은 나타나고 있었지만 모델간의 불확실성은 여름철에 크게 나타나고 있었다. 역학적 및 통계적 상세화 기후 시나리오(ECHO-G/S, A2)를 비교 분석한 결과, 기온은 역학적 상세화 결과가 약간 크게 나타났으며, 전체적으로 유사한 패턴을 보이고 있었다. 강수량 또한 역학적 상세화 결과가 크게 나타나고 있었다. 역학적 및 통계적 상세화 결과는 S1의 경우 유사한 특징을 보이고 있었지만 S3로 갈수록 차이가 크게 나타나고 있었다.